Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Spatiotemporal evolution of the ccRCC microenvironment links intra-tumoral heterogeneity to immune escape CINOMA

ABSTRACT: Spatiotemporal evolution of the ccRCC microenvironment links intra-tumoral heterogeneity to immune escape CINOMA

PROVIDER: PRJNA888195 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Similar Datasets

Spatiotemporal evolution of the ccRCC microenvironment links intra-tumoral heterogeneity to immune escape CINOMA

Project description:Spatiotemporal evolution of the ccRCC microenvironment links intra-tumoral heterogeneity to immune escape CINOMA

| PRJNA888194 | ENA

Intra-individual tumoral heterogeneity of ovarian serous adenocarcinomas

Project description:Intra-individual tumoral heterogeneity of ovarian serous adenocarcinomas

| PRJNA338120 | ENA

Transcription profiling of biopsies from human squamous cell carcinomas to quantify intra-tumoral heterogeneity

Project description:Heterogeneity of gene expression profiles in head and neck cancer Background: Results of gene expression profiling studies from different institutes often lack consistency. This could be due to the use of different microarray platforms and protocols, or to intra-tumoral heterogeneity in mRNA expression. The aim of our study was to quantify intra-tumoral heterogeneity in head and cancer. Methods: Forty-four fresh frozen biopsies were taken from 22 patients, two per tumor. RNA was extracted, tested for quality, amplified, labeled and hybridized to a 35k oligoarray. Results: Unsupervised clustering analyses using all genes, the most variable genes, or random gene sets showed that 80-90% of biopsy pairs clustered together. A within-pair-between-pair scatter ratio analysis showed that the similarity between matching pairs was significantly greater than that between random pairs (p < 0.00001). Conclusions: Two biopsies from the same tumor show far greater similarity in gene expression than biopsies from different tumors, supporting the use of one biopsy for expression profiling.

2008-02-19 | E-NCMF-7 | biostudies-arrayexpress

Integrating the intra-tumoral microbiome to promote understanding on tumor plasticity

Project description:Intra-tumoral microbiome in colorectal cancer patients and tumor plasticity

| PRJEB71998 | ENA

Expression data from different samples of a single ccRCC patient: 7 primary intra-tumor samples and 2 metastatic tumor samples

2011-08-25 | GSE31610 | GEO

IFN gamma signaling in cytotoxic T cells restricts antitumor responses by inhibiting the maintenance and clonality of intra-tumoral stem-like T cells

Project description:IFN gamma signaling in cytotoxic T cells restricts antitumor responses by inhibiting the maintenance and clonality of intra-tumoral stem-like T cells

2022-12-16 | GSE221118 | GEO

Expression data from different samples of a single ccRCC patient: 8 primary intra-tumor samples and 2 metastatic tumor samples

Project description:We have sampled several tumour regions from a single ccRCC patient after 6 weeks everolimus treatment, including both primary and metastatic site samples to investigate intra-tumour heterogeneity. We selected 8 samples from the primary tumour, and 2 samples from a chestwall metastatic site for expression analysis using microarray data. All samples were fresh frozen upon extraction.

2011-08-24 | E-GEOD-31610 | biostudies-arrayexpress

Trunk events present minimal intra- and inter-tumoral heterogeneity in hepatocellular carcinoma

Project description:Trunk events present minimal intra- and inter-tumoral heterogeneity in hepatocellular carcinoma

| PRJNA385729 | ENA

Topographic mapping of the glioblastoma proteome reveals a triple axis model of intra-tumoral heterogeneity

Project description:Glioblastoma (GBM) is an aggressive form of brain cancer with well-established patterns of intra-tumoral heterogeneity implicated in treatment resistance, recurrence and progression. While regional and single cell transcriptomic variations of GBM have been recently resolved, downstream phenotype-level proteomic programs have yet to be assigned to specific niches. Here, we leverage mass spectrometry to spatially align abundance levels of 4,794 proteins to GBM’s hallmark histomorphologic niches across 20 patients and define distinct molecular programs operational within these regional tumor compartments. Using machine learning, we overlay concordant transcriptional information, and define two distinct proteogenomic programs, MYC- and KRAS-axis hereon, that cooperate with hypoxia to produce a tri-dimensional model of intra-tumoral heterogeneity. Importantly, we show using multiple cohorts, that GBMs with an enhanced KRAS component harbor a more clinically aggressive and infiltrative phenotype. Conversely, tumor cells enriched along the MYC axis where mutually exclusive and had a distinct proliferative program. Moreover, by applying both experimental and computational approaches to link each of these distinct molecular axes with potential pharmacological therapies, we highlight differential drug sensitivities and a notable relative chemoresistance in GBM cell lines with enhanced KRAS programs. Importantly, pharmacological differences were less evident when using traditional expression-based subgroups supporting thattopographic phenotypic mapping ofGBM, and the proposed axes may provide new insights for targeting heterogeneity and overcoming therapy resistance in this aggressive disease.

2021-11-02 | PXD029290 | Pride

Transcription profiling by array of human Glioblastoma multiforme (GBM) tumor samples to asses the intra-tumoral heterogeneity at the gene expression level

Project description:Glioblastoma multiforme (GBM) is a highly heterogeneous disease that shows an enourmous range of genetic abnormalities in comparison to other astrocytic tumors. Intra-patient heterogeneity in GBM has been poorly characterized both at phenotypic and genomic level. During surgical GBM resections, we have extracted between 4 and 8 tumor subsamples from different areas of the malignant tissue that were at least 1cm apart. Our aim to asses the intra-tumoral heterogeneity at the gene expression level to uncover important dynamics underlying GBM progression that may have relevant implication for treatment.

2013-02-16 | E-MTAB-1129 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data